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Abstract

Background Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of Traditional Chinese Medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of Traditional Chinese Medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets.

Results We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of Traditional Chinese Medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization.

Conclusions Our analysis suggests that datasets of molecular ingredients of Traditional Chinese Medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients.

Supplemental Information

Supplementary Table 1

Chinese medicine molecules used in the present study with their smiles.

Supplementary Table 3

Additional Information

Competing Interests

Vinod Scaria is an Academic Editor for PeerJ and works for the Open Source Drug Discovery Project/Consortium.

Author Contributions

Salma Jamal performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Vinod Scaria conceived and designed the experiments, wrote the paper, reviewed drafts of the paper.

Grant Disclosures

The following grant information was disclosed by the authors:

Open Source Drug Discovery Project (HCP001).

Data Deposition

The following information was supplied regarding the deposition of related data:

http://vinodscaria.rnabiology.org/2C4C/models

Funding

This work was funded by the Council of Scientific and Industrial Research (CSIR), India through Open Source Drug Discovery Project (HCP001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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